.png){kind=icon}
返回
Optimizing climate-induced migration: A temporal multi-layer network approach
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ijdrr.2024.105172.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Resettlement planning for refugees has historically focused on immediate, short-term solutions, often neglecting the essential foresight needed for mid-term and long-term accommodations. To adequately prepare for long-term dynamics, it is crucial to account for the changing conditions and needs that could impact resource requirements. In response, we propose a model to provide solutions considering changing demands and capacities in resettlement planning. Inspired by the concept of temporal networks, our method constructs a time-dependent network divided into various temporal layers. Each layer serves as a static depiction of the network at specific intervals, capturing the network's evolution and reflecting the shifting resettlement needs. We solve each layer's minimum-cost flow problem and apply the results across different periods. Beyond the logistics of actual refugee movements, our model integrates the notion of 'integration success as a critical measure, evaluating the effectiveness of resettlement in facilitating refugees' smooth transition into new communities. A high integration success score reflects the successful assimilation of a significant refugee population. We detail an mixed-integer programming model to implement this approach, demonstrate its capability, and assess its performance across various scenarios. This advanced model offers a comprehensive framework for planning refugee resettlement that balances immediate needs with long-term integration results. Our results reveal that the model provides insights by dynamically adjusting supplies and capacities based on previous periods. These adjustments allow for strategic delays and modifications in relocation flows to optimize the relocation process.
Keyword:
Refugee resettlement
Climate change
Temporal networks
Humanitarian logistics
Optimization
期刊
IF:
4.5
论文数:
6.1K
被引数:
2.1W
机构
引用论文
New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding.png)
NATURE COMMUNICATIONS
OAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏Lost in transportation: Information measures and cognitive limits in multilayer navigationSCIENCE ADVANCESOAAI
SCIENCE ADVANCESIF12.5
OAAI分享收藏分享收藏
查看更多内容